Overview

Dataset statistics

Number of variables42
Number of observations1470
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory482.5 KiB
Average record size in memory336.1 B

Variable types

Categorical31
Numeric11

Alerts

age is highly overall correlated with annee_experience_totaleHigh correlation
annee_experience_totale is highly overall correlated with age and 4 other fieldsHigh correlation
annees_dans_l_entreprise is highly overall correlated with annee_experience_totale and 3 other fieldsHigh correlation
annees_dans_le_poste_actuel is highly overall correlated with annees_dans_l_entreprise and 2 other fieldsHigh correlation
annees_depuis_la_derniere_promotion is highly overall correlated with annees_dans_l_entreprise and 1 other fieldsHigh correlation
annes_sous_responsable_actuel is highly overall correlated with annees_dans_l_entreprise and 1 other fieldsHigh correlation
augementation_salaire_precedente is highly overall correlated with note_evaluation_actuelleHigh correlation
departement_Consulting is highly overall correlated with poste_Cadre CommercialHigh correlation
departement_Ressources Humaines is highly overall correlated with domaine_etude_Ressources Humaines and 1 other fieldsHigh correlation
domaine_etude_Infra & Cloud is highly overall correlated with domaine_etude_Transformation DigitaleHigh correlation
domaine_etude_Ressources Humaines is highly overall correlated with departement_Ressources Humaines and 1 other fieldsHigh correlation
domaine_etude_Transformation Digitale is highly overall correlated with domaine_etude_Infra & CloudHigh correlation
frequence_deplacement_Frequent is highly overall correlated with frequence_deplacement_OccasionnelHigh correlation
frequence_deplacement_Occasionnel is highly overall correlated with frequence_deplacement_FrequentHigh correlation
niveau_hierarchique_poste is highly overall correlated with annee_experience_totale and 2 other fieldsHigh correlation
note_evaluation_actuelle is highly overall correlated with augementation_salaire_precedenteHigh correlation
poste_Cadre Commercial is highly overall correlated with departement_ConsultingHigh correlation
poste_Directeur Technique is highly overall correlated with revenu_mensuelHigh correlation
poste_Ressources Humaines is highly overall correlated with departement_Ressources Humaines and 1 other fieldsHigh correlation
poste_Senior Manager is highly overall correlated with annee_experience_totale and 2 other fieldsHigh correlation
revenu_mensuel is highly overall correlated with annee_experience_totale and 3 other fieldsHigh correlation
domaine_etude_Entrepreunariat is highly imbalanced (56.4%)Imbalance
domaine_etude_Marketing is highly imbalanced (50.6%)Imbalance
domaine_etude_Ressources Humaines is highly imbalanced (86.8%)Imbalance
poste_Directeur Technique is highly imbalanced (69.5%)Imbalance
poste_Manager is highly imbalanced (56.6%)Imbalance
poste_Représentant Commercial is highly imbalanced (68.7%)Imbalance
poste_Ressources Humaines is highly imbalanced (77.9%)Imbalance
poste_Senior Manager is highly imbalanced (63.6%)Imbalance
poste_Tech Lead is highly imbalanced (53.5%)Imbalance
departement_Ressources Humaines is highly imbalanced (74.5%)Imbalance
nombre_experiences_precedentes has 197 (13.4%) zerosZeros
annees_dans_l_entreprise has 44 (3.0%) zerosZeros
annees_dans_le_poste_actuel has 244 (16.6%) zerosZeros
nb_formations_suivies has 54 (3.7%) zerosZeros
annees_depuis_la_derniere_promotion has 581 (39.5%) zerosZeros
annes_sous_responsable_actuel has 263 (17.9%) zerosZeros

Reproduction

Analysis started2025-12-30 22:11:17.854197
Analysis finished2025-12-30 22:12:19.586822
Duration1 minute and 1.73 second
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
453 
4
446 
2
287 
1
284 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row4
4th row4
5th row1

Common Values

ValueCountFrequency (%)
3453
30.8%
4446
30.3%
2287
19.5%
1284
19.3%

Length

2025-12-30T23:12:19.782223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-30T23:12:20.046115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3453
30.8%
4446
30.3%
2287
19.5%
1284
19.3%

Most occurring characters

ValueCountFrequency (%)
3453
30.8%
4446
30.3%
2287
19.5%
1284
19.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3453
30.8%
4446
30.3%
2287
19.5%
1284
19.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3453
30.8%
4446
30.3%
2287
19.5%
1284
19.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3453
30.8%
4446
30.3%
2287
19.5%
1284
19.3%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
868 
2
375 
4
144 
1
 
83

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3868
59.0%
2375
25.5%
4144
 
9.8%
183
 
5.6%

Length

2025-12-30T23:12:20.540408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-30T23:12:20.735870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3868
59.0%
2375
25.5%
4144
 
9.8%
183
 
5.6%

Most occurring characters

ValueCountFrequency (%)
3868
59.0%
2375
25.5%
4144
 
9.8%
183
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3868
59.0%
2375
25.5%
4144
 
9.8%
183
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3868
59.0%
2375
25.5%
4144
 
9.8%
183
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3868
59.0%
2375
25.5%
4144
 
9.8%
183
 
5.6%

niveau_hierarchique_poste
Categorical

High correlation 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
1
543 
2
534 
3
218 
4
106 
5
69 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1543
36.9%
2534
36.3%
3218
14.8%
4106
 
7.2%
569
 
4.7%

Length

2025-12-30T23:12:20.985164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-30T23:12:21.182710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1543
36.9%
2534
36.3%
3218
14.8%
4106
 
7.2%
569
 
4.7%

Most occurring characters

ValueCountFrequency (%)
1543
36.9%
2534
36.3%
3218
14.8%
4106
 
7.2%
569
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1543
36.9%
2534
36.3%
3218
14.8%
4106
 
7.2%
569
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1543
36.9%
2534
36.3%
3218
14.8%
4106
 
7.2%
569
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1543
36.9%
2534
36.3%
3218
14.8%
4106
 
7.2%
569
 
4.7%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
4
459 
3
442 
1
289 
2
280 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row2
3rd row3
4th row3
5th row2

Common Values

ValueCountFrequency (%)
4459
31.2%
3442
30.1%
1289
19.7%
2280
19.0%

Length

2025-12-30T23:12:21.480042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-30T23:12:21.642547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
4459
31.2%
3442
30.1%
1289
19.7%
2280
19.0%

Most occurring characters

ValueCountFrequency (%)
4459
31.2%
3442
30.1%
1289
19.7%
2280
19.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4459
31.2%
3442
30.1%
1289
19.7%
2280
19.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4459
31.2%
3442
30.1%
1289
19.7%
2280
19.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4459
31.2%
3442
30.1%
1289
19.7%
2280
19.0%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
459 
4
432 
2
303 
1
276 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row4
3rd row2
4th row3
5th row4

Common Values

ValueCountFrequency (%)
3459
31.2%
4432
29.4%
2303
20.6%
1276
18.8%

Length

2025-12-30T23:12:21.910795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-30T23:12:22.086360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3459
31.2%
4432
29.4%
2303
20.6%
1276
18.8%

Most occurring characters

ValueCountFrequency (%)
3459
31.2%
4432
29.4%
2303
20.6%
1276
18.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3459
31.2%
4432
29.4%
2303
20.6%
1276
18.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3459
31.2%
4432
29.4%
2303
20.6%
1276
18.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3459
31.2%
4432
29.4%
2303
20.6%
1276
18.8%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
893 
2
344 
4
153 
1
 
80

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3893
60.7%
2344
 
23.4%
4153
 
10.4%
180
 
5.4%

Length

2025-12-30T23:12:22.296736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-30T23:12:22.487751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3893
60.7%
2344
 
23.4%
4153
 
10.4%
180
 
5.4%

Most occurring characters

ValueCountFrequency (%)
3893
60.7%
2344
 
23.4%
4153
 
10.4%
180
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3893
60.7%
2344
 
23.4%
4153
 
10.4%
180
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3893
60.7%
2344
 
23.4%
4153
 
10.4%
180
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3893
60.7%
2344
 
23.4%
4153
 
10.4%
180
 
5.4%

note_evaluation_actuelle
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
1244 
4
226 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row4
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
31244
84.6%
4226
 
15.4%

Length

2025-12-30T23:12:22.719754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-30T23:12:22.892616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
31244
84.6%
4226
 
15.4%

Most occurring characters

ValueCountFrequency (%)
31244
84.6%
4226
 
15.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
31244
84.6%
4226
 
15.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
31244
84.6%
4226
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
31244
84.6%
4226
 
15.4%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
1054 
1
416 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
01054
71.7%
1416
 
28.3%

Length

2025-12-30T23:12:23.097649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-30T23:12:23.263561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01054
71.7%
1416
 
28.3%

Most occurring characters

ValueCountFrequency (%)
01054
71.7%
1416
 
28.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01054
71.7%
1416
 
28.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01054
71.7%
1416
 
28.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01054
71.7%
1416
 
28.3%

augementation_salaire_precedente
Real number (ℝ)

High correlation 

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.209524
Minimum11
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-12-30T23:12:23.437803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q112
median14
Q318
95-th percentile22
Maximum25
Range14
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.6599377
Coefficient of variation (CV)0.2406346
Kurtosis-0.30059822
Mean15.209524
Median Absolute Deviation (MAD)2
Skewness0.82112798
Sum22358
Variance13.395144
MonotonicityNot monotonic
2025-12-30T23:12:23.629654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
11210
14.3%
13209
14.2%
14201
13.7%
12198
13.5%
15101
6.9%
1889
6.1%
1782
 
5.6%
1678
 
5.3%
1976
 
5.2%
2256
 
3.8%
Other values (5)170
11.6%
ValueCountFrequency (%)
11210
14.3%
12198
13.5%
13209
14.2%
14201
13.7%
15101
6.9%
1678
 
5.3%
1782
 
5.6%
1889
6.1%
1976
 
5.2%
2055
 
3.7%
ValueCountFrequency (%)
2518
 
1.2%
2421
 
1.4%
2328
 
1.9%
2256
3.8%
2148
3.3%
2055
3.7%
1976
5.2%
1889
6.1%
1782
5.6%
1678
5.3%

age
Real number (ℝ)

High correlation 

Distinct43
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.92381
Minimum18
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-12-30T23:12:23.874016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile24
Q130
median36
Q343
95-th percentile54
Maximum60
Range42
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.1353735
Coefficient of variation (CV)0.24741146
Kurtosis-0.40414514
Mean36.92381
Median Absolute Deviation (MAD)6
Skewness0.4132863
Sum54278
Variance83.455049
MonotonicityNot monotonic
2025-12-30T23:12:24.181386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3578
 
5.3%
3477
 
5.2%
3669
 
4.7%
3169
 
4.7%
2968
 
4.6%
3261
 
4.1%
3060
 
4.1%
3358
 
3.9%
3858
 
3.9%
4057
 
3.9%
Other values (33)815
55.4%
ValueCountFrequency (%)
188
 
0.5%
199
 
0.6%
2011
 
0.7%
2113
 
0.9%
2216
 
1.1%
2314
 
1.0%
2426
1.8%
2526
1.8%
2639
2.7%
2748
3.3%
ValueCountFrequency (%)
605
 
0.3%
5910
0.7%
5814
1.0%
574
 
0.3%
5614
1.0%
5522
1.5%
5418
1.2%
5319
1.3%
5218
1.2%
5119
1.3%

genre
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
882 
1
588 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0882
60.0%
1588
40.0%

Length

2025-12-30T23:12:24.455690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-30T23:12:24.639674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0882
60.0%
1588
40.0%

Most occurring characters

ValueCountFrequency (%)
0882
60.0%
1588
40.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0882
60.0%
1588
40.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0882
60.0%
1588
40.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0882
60.0%
1588
40.0%

revenu_mensuel
Real number (ℝ)

High correlation 

Distinct1349
Distinct (%)91.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6502.9313
Minimum1009
Maximum19999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-12-30T23:12:24.841599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1009
5-th percentile2097.9
Q12911
median4919
Q38379
95-th percentile17821.35
Maximum19999
Range18990
Interquartile range (IQR)5468

Descriptive statistics

Standard deviation4707.9568
Coefficient of variation (CV)0.72397455
Kurtosis1.0052327
Mean6502.9313
Median Absolute Deviation (MAD)2199
Skewness1.3698167
Sum9559309
Variance22164857
MonotonicityNot monotonic
2025-12-30T23:12:25.185138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23424
 
0.3%
61423
 
0.2%
27413
 
0.2%
25593
 
0.2%
26103
 
0.2%
24513
 
0.2%
55623
 
0.2%
34523
 
0.2%
23803
 
0.2%
63473
 
0.2%
Other values (1339)1439
97.9%
ValueCountFrequency (%)
10091
0.1%
10511
0.1%
10521
0.1%
10811
0.1%
10911
0.1%
11021
0.1%
11181
0.1%
11291
0.1%
12001
0.1%
12231
0.1%
ValueCountFrequency (%)
199991
0.1%
199731
0.1%
199431
0.1%
199261
0.1%
198591
0.1%
198471
0.1%
198451
0.1%
198331
0.1%
197401
0.1%
197171
0.1%

nombre_experiences_precedentes
Real number (ℝ)

Zeros 

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6931973
Minimum0
Maximum9
Zeros197
Zeros (%)13.4%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-12-30T23:12:25.423452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.498009
Coefficient of variation (CV)0.92752545
Kurtosis0.010213817
Mean2.6931973
Median Absolute Deviation (MAD)1
Skewness1.0264711
Sum3959
Variance6.240049
MonotonicityNot monotonic
2025-12-30T23:12:25.619195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1521
35.4%
0197
 
13.4%
3159
 
10.8%
2146
 
9.9%
4139
 
9.5%
774
 
5.0%
670
 
4.8%
563
 
4.3%
952
 
3.5%
849
 
3.3%
ValueCountFrequency (%)
0197
 
13.4%
1521
35.4%
2146
 
9.9%
3159
 
10.8%
4139
 
9.5%
563
 
4.3%
670
 
4.8%
774
 
5.0%
849
 
3.3%
952
 
3.5%
ValueCountFrequency (%)
952
 
3.5%
849
 
3.3%
774
 
5.0%
670
 
4.8%
563
 
4.3%
4139
 
9.5%
3159
 
10.8%
2146
 
9.9%
1521
35.4%
0197
 
13.4%

annee_experience_totale
Real number (ℝ)

High correlation 

Distinct40
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.279592
Minimum0
Maximum40
Zeros11
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-12-30T23:12:25.886767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median10
Q315
95-th percentile28
Maximum40
Range40
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.7807817
Coefficient of variation (CV)0.68981057
Kurtosis0.91826954
Mean11.279592
Median Absolute Deviation (MAD)4
Skewness1.1171719
Sum16581
Variance60.540563
MonotonicityNot monotonic
2025-12-30T23:12:26.163187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
10202
 
13.7%
6125
 
8.5%
8103
 
7.0%
996
 
6.5%
588
 
6.0%
781
 
5.5%
181
 
5.5%
463
 
4.3%
1248
 
3.3%
342
 
2.9%
Other values (30)541
36.8%
ValueCountFrequency (%)
011
 
0.7%
181
5.5%
231
 
2.1%
342
 
2.9%
463
4.3%
588
6.0%
6125
8.5%
781
5.5%
8103
7.0%
996
6.5%
ValueCountFrequency (%)
402
 
0.1%
381
 
0.1%
374
0.3%
366
0.4%
353
 
0.2%
345
0.3%
337
0.5%
329
0.6%
319
0.6%
307
0.5%

annees_dans_l_entreprise
Real number (ℝ)

High correlation  Zeros 

Distinct37
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0081633
Minimum0
Maximum40
Zeros44
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-12-30T23:12:26.416066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q39
95-th percentile20
Maximum40
Range40
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.1265252
Coefficient of variation (CV)0.87419841
Kurtosis3.9355088
Mean7.0081633
Median Absolute Deviation (MAD)3
Skewness1.7645295
Sum10302
Variance37.53431
MonotonicityNot monotonic
2025-12-30T23:12:26.675228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
5196
13.3%
1171
11.6%
3128
8.7%
2127
8.6%
10120
8.2%
4110
 
7.5%
790
 
6.1%
982
 
5.6%
880
 
5.4%
676
 
5.2%
Other values (27)290
19.7%
ValueCountFrequency (%)
044
 
3.0%
1171
11.6%
2127
8.6%
3128
8.7%
4110
7.5%
5196
13.3%
676
 
5.2%
790
6.1%
880
5.4%
982
5.6%
ValueCountFrequency (%)
401
 
0.1%
371
 
0.1%
362
 
0.1%
341
 
0.1%
335
0.3%
323
0.2%
313
0.2%
301
 
0.1%
292
 
0.1%
272
 
0.1%

annees_dans_le_poste_actuel
Real number (ℝ)

High correlation  Zeros 

Distinct19
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2292517
Minimum0
Maximum18
Zeros244
Zeros (%)16.6%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-12-30T23:12:26.897135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile11
Maximum18
Range18
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.623137
Coefficient of variation (CV)0.85668513
Kurtosis0.47742077
Mean4.2292517
Median Absolute Deviation (MAD)3
Skewness0.91736316
Sum6217
Variance13.127122
MonotonicityNot monotonic
2025-12-30T23:12:27.137726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2372
25.3%
0244
16.6%
7222
15.1%
3135
 
9.2%
4104
 
7.1%
889
 
6.1%
967
 
4.6%
157
 
3.9%
637
 
2.5%
536
 
2.4%
Other values (9)107
 
7.3%
ValueCountFrequency (%)
0244
16.6%
157
 
3.9%
2372
25.3%
3135
 
9.2%
4104
 
7.1%
536
 
2.4%
637
 
2.5%
7222
15.1%
889
 
6.1%
967
 
4.6%
ValueCountFrequency (%)
182
 
0.1%
174
 
0.3%
167
 
0.5%
158
 
0.5%
1411
 
0.7%
1314
 
1.0%
1210
 
0.7%
1122
 
1.5%
1029
2.0%
967
4.6%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
1233 
1
237 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01233
83.9%
1237
 
16.1%

Length

2025-12-30T23:12:27.462676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-30T23:12:27.601993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01233
83.9%
1237
 
16.1%

Most occurring characters

ValueCountFrequency (%)
01233
83.9%
1237
 
16.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01233
83.9%
1237
 
16.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01233
83.9%
1237
 
16.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01233
83.9%
1237
 
16.1%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
631 
1
596 
2
158 
3
85 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0631
42.9%
1596
40.5%
2158
 
10.7%
385
 
5.8%

Length

2025-12-30T23:12:27.884502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-30T23:12:28.078983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0631
42.9%
1596
40.5%
2158
 
10.7%
385
 
5.8%

Most occurring characters

ValueCountFrequency (%)
0631
42.9%
1596
40.5%
2158
 
10.7%
385
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0631
42.9%
1596
40.5%
2158
 
10.7%
385
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0631
42.9%
1596
40.5%
2158
 
10.7%
385
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0631
42.9%
1596
40.5%
2158
 
10.7%
385
 
5.8%

nb_formations_suivies
Real number (ℝ)

Zeros 

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7993197
Minimum0
Maximum6
Zeros54
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-12-30T23:12:28.287077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2892706
Coefficient of variation (CV)0.46056569
Kurtosis0.49499299
Mean2.7993197
Median Absolute Deviation (MAD)1
Skewness0.55312417
Sum4115
Variance1.6622187
MonotonicityNot monotonic
2025-12-30T23:12:28.517715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2547
37.2%
3491
33.4%
4123
 
8.4%
5119
 
8.1%
171
 
4.8%
665
 
4.4%
054
 
3.7%
ValueCountFrequency (%)
054
 
3.7%
171
 
4.8%
2547
37.2%
3491
33.4%
4123
 
8.4%
5119
 
8.1%
665
 
4.4%
ValueCountFrequency (%)
665
 
4.4%
5119
 
8.1%
4123
 
8.4%
3491
33.4%
2547
37.2%
171
 
4.8%
054
 
3.7%

distance_domicile_travail
Real number (ℝ)

Distinct29
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.192517
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-12-30T23:12:28.745634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q314
95-th percentile26
Maximum29
Range28
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.1068644
Coefficient of variation (CV)0.88189823
Kurtosis-0.2248334
Mean9.192517
Median Absolute Deviation (MAD)5
Skewness0.958118
Sum13513
Variance65.721251
MonotonicityNot monotonic
2025-12-30T23:12:28.985275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2211
14.4%
1208
14.1%
1086
 
5.9%
985
 
5.8%
384
 
5.7%
784
 
5.7%
880
 
5.4%
565
 
4.4%
464
 
4.4%
659
 
4.0%
Other values (19)444
30.2%
ValueCountFrequency (%)
1208
14.1%
2211
14.4%
384
 
5.7%
464
 
4.4%
565
 
4.4%
659
 
4.0%
784
 
5.7%
880
 
5.4%
985
5.8%
1086
5.9%
ValueCountFrequency (%)
2927
1.8%
2823
1.6%
2712
0.8%
2625
1.7%
2525
1.7%
2428
1.9%
2327
1.8%
2219
1.3%
2118
1.2%
2025
1.7%

niveau_education
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
3
572 
4
398 
2
282 
1
170 
5
 
48

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row4
5th row1

Common Values

ValueCountFrequency (%)
3572
38.9%
4398
27.1%
2282
19.2%
1170
 
11.6%
548
 
3.3%

Length

2025-12-30T23:12:29.283418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-30T23:12:29.481347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3572
38.9%
4398
27.1%
2282
19.2%
1170
 
11.6%
548
 
3.3%

Most occurring characters

ValueCountFrequency (%)
3572
38.9%
4398
27.1%
2282
19.2%
1170
 
11.6%
548
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3572
38.9%
4398
27.1%
2282
19.2%
1170
 
11.6%
548
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3572
38.9%
4398
27.1%
2282
19.2%
1170
 
11.6%
548
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3572
38.9%
4398
27.1%
2282
19.2%
1170
 
11.6%
548
 
3.3%

annees_depuis_la_derniere_promotion
Real number (ℝ)

High correlation  Zeros 

Distinct16
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1877551
Minimum0
Maximum15
Zeros581
Zeros (%)39.5%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-12-30T23:12:29.724777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile9
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.2224303
Coefficient of variation (CV)1.4729392
Kurtosis3.6126731
Mean2.1877551
Median Absolute Deviation (MAD)1
Skewness1.98429
Sum3216
Variance10.384057
MonotonicityNot monotonic
2025-12-30T23:12:30.005812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0581
39.5%
1357
24.3%
2159
 
10.8%
776
 
5.2%
461
 
4.1%
352
 
3.5%
545
 
3.1%
632
 
2.2%
1124
 
1.6%
818
 
1.2%
Other values (6)65
 
4.4%
ValueCountFrequency (%)
0581
39.5%
1357
24.3%
2159
 
10.8%
352
 
3.5%
461
 
4.1%
545
 
3.1%
632
 
2.2%
776
 
5.2%
818
 
1.2%
917
 
1.2%
ValueCountFrequency (%)
1513
 
0.9%
149
 
0.6%
1310
 
0.7%
1210
 
0.7%
1124
 
1.6%
106
 
0.4%
917
 
1.2%
818
 
1.2%
776
5.2%
632
2.2%

annes_sous_responsable_actuel
Real number (ℝ)

High correlation  Zeros 

Distinct18
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1231293
Minimum0
Maximum17
Zeros263
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size11.6 KiB
2025-12-30T23:12:30.207085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.5681361
Coefficient of variation (CV)0.86539517
Kurtosis0.17105808
Mean4.1231293
Median Absolute Deviation (MAD)3
Skewness0.83345099
Sum6061
Variance12.731595
MonotonicityNot monotonic
2025-12-30T23:12:30.424612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2344
23.4%
0263
17.9%
7216
14.7%
3142
9.7%
8107
 
7.3%
498
 
6.7%
176
 
5.2%
964
 
4.4%
531
 
2.1%
629
 
2.0%
Other values (8)100
 
6.8%
ValueCountFrequency (%)
0263
17.9%
176
 
5.2%
2344
23.4%
3142
9.7%
498
 
6.7%
531
 
2.1%
629
 
2.0%
7216
14.7%
8107
 
7.3%
964
 
4.4%
ValueCountFrequency (%)
177
 
0.5%
162
 
0.1%
155
 
0.3%
145
 
0.3%
1314
 
1.0%
1218
 
1.2%
1122
 
1.5%
1027
 
1.8%
964
4.4%
8107
7.3%

domaine_etude_Entrepreunariat
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
1338 
1
 
132

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01338
91.0%
1132
 
9.0%

Length

2025-12-30T23:12:30.698111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-30T23:12:30.834843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01338
91.0%
1132
 
9.0%

Most occurring characters

ValueCountFrequency (%)
01338
91.0%
1132
 
9.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01338
91.0%
1132
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01338
91.0%
1132
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01338
91.0%
1132
 
9.0%

domaine_etude_Infra & Cloud
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
864 
1
606 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0864
58.8%
1606
41.2%

Length

2025-12-30T23:12:31.066665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-30T23:12:31.227319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0864
58.8%
1606
41.2%

Most occurring characters

ValueCountFrequency (%)
0864
58.8%
1606
41.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0864
58.8%
1606
41.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0864
58.8%
1606
41.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0864
58.8%
1606
41.2%

domaine_etude_Marketing
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
1311 
1
159 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01311
89.2%
1159
 
10.8%

Length

2025-12-30T23:12:31.455481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-30T23:12:31.624499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01311
89.2%
1159
 
10.8%

Most occurring characters

ValueCountFrequency (%)
01311
89.2%
1159
 
10.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01311
89.2%
1159
 
10.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01311
89.2%
1159
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01311
89.2%
1159
 
10.8%

domaine_etude_Ressources Humaines
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
1443 
1
 
27

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01443
98.2%
127
 
1.8%

Length

2025-12-30T23:12:31.838539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-30T23:12:31.983755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01443
98.2%
127
 
1.8%

Most occurring characters

ValueCountFrequency (%)
01443
98.2%
127
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01443
98.2%
127
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01443
98.2%
127
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01443
98.2%
127
 
1.8%

domaine_etude_Transformation Digitale
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
1006 
1
464 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
01006
68.4%
1464
31.6%

Length

2025-12-30T23:12:32.178513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-30T23:12:32.350253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01006
68.4%
1464
31.6%

Most occurring characters

ValueCountFrequency (%)
01006
68.4%
1464
31.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01006
68.4%
1464
31.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01006
68.4%
1464
31.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01006
68.4%
1464
31.6%

frequence_deplacement_Frequent
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
1193 
1
277 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
01193
81.2%
1277
 
18.8%

Length

2025-12-30T23:12:32.560966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-30T23:12:32.729692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01193
81.2%
1277
 
18.8%

Most occurring characters

ValueCountFrequency (%)
01193
81.2%
1277
 
18.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01193
81.2%
1277
 
18.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01193
81.2%
1277
 
18.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01193
81.2%
1277
 
18.8%

frequence_deplacement_Occasionnel
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
1
1043 
0
427 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
11043
71.0%
0427
29.0%

Length

2025-12-30T23:12:32.932497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-30T23:12:33.106725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
11043
71.0%
0427
29.0%

Most occurring characters

ValueCountFrequency (%)
11043
71.0%
0427
29.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11043
71.0%
0427
29.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11043
71.0%
0427
29.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11043
71.0%
0427
29.0%

poste_Cadre Commercial
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
1144 
1
326 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01144
77.8%
1326
 
22.2%

Length

2025-12-30T23:12:33.332752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-30T23:12:33.498901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01144
77.8%
1326
 
22.2%

Most occurring characters

ValueCountFrequency (%)
01144
77.8%
1326
 
22.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01144
77.8%
1326
 
22.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01144
77.8%
1326
 
22.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01144
77.8%
1326
 
22.2%

poste_Consultant
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
1211 
1
259 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
01211
82.4%
1259
 
17.6%

Length

2025-12-30T23:12:33.709535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-30T23:12:33.848896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01211
82.4%
1259
 
17.6%

Most occurring characters

ValueCountFrequency (%)
01211
82.4%
1259
 
17.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01211
82.4%
1259
 
17.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01211
82.4%
1259
 
17.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01211
82.4%
1259
 
17.6%

poste_Directeur Technique
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
1390 
1
 
80

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01390
94.6%
180
 
5.4%

Length

2025-12-30T23:12:34.777296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-30T23:12:34.949596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01390
94.6%
180
 
5.4%

Most occurring characters

ValueCountFrequency (%)
01390
94.6%
180
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01390
94.6%
180
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01390
94.6%
180
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01390
94.6%
180
 
5.4%

poste_Manager
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
1339 
1
 
131

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01339
91.1%
1131
 
8.9%

Length

2025-12-30T23:12:35.193555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-30T23:12:35.361499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01339
91.1%
1131
 
8.9%

Most occurring characters

ValueCountFrequency (%)
01339
91.1%
1131
 
8.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01339
91.1%
1131
 
8.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01339
91.1%
1131
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01339
91.1%
1131
 
8.9%

poste_Représentant Commercial
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
1387 
1
 
83

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01387
94.4%
183
 
5.6%

Length

2025-12-30T23:12:35.638458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-30T23:12:35.837227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01387
94.4%
183
 
5.6%

Most occurring characters

ValueCountFrequency (%)
01387
94.4%
183
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01387
94.4%
183
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01387
94.4%
183
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01387
94.4%
183
 
5.6%

poste_Ressources Humaines
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
1418 
1
 
52

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01418
96.5%
152
 
3.5%

Length

2025-12-30T23:12:36.090132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-30T23:12:36.262527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01418
96.5%
152
 
3.5%

Most occurring characters

ValueCountFrequency (%)
01418
96.5%
152
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01418
96.5%
152
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01418
96.5%
152
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01418
96.5%
152
 
3.5%

poste_Senior Manager
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
1368 
1
 
102

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01368
93.1%
1102
 
6.9%

Length

2025-12-30T23:12:36.474715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-30T23:12:36.654998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01368
93.1%
1102
 
6.9%

Most occurring characters

ValueCountFrequency (%)
01368
93.1%
1102
 
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01368
93.1%
1102
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01368
93.1%
1102
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01368
93.1%
1102
 
6.9%

poste_Tech Lead
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
1325 
1
145 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01325
90.1%
1145
 
9.9%

Length

2025-12-30T23:12:36.870759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-30T23:12:37.036590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01325
90.1%
1145
 
9.9%

Most occurring characters

ValueCountFrequency (%)
01325
90.1%
1145
 
9.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01325
90.1%
1145
 
9.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01325
90.1%
1145
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01325
90.1%
1145
 
9.9%

departement_Consulting
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
1
961 
0
509 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1961
65.4%
0509
34.6%

Length

2025-12-30T23:12:37.228937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-30T23:12:37.364709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1961
65.4%
0509
34.6%

Most occurring characters

ValueCountFrequency (%)
1961
65.4%
0509
34.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1961
65.4%
0509
34.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1961
65.4%
0509
34.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1961
65.4%
0509
34.6%

departement_Ressources Humaines
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
1407 
1
 
63

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01407
95.7%
163
 
4.3%

Length

2025-12-30T23:12:37.602524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-30T23:12:37.767867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01407
95.7%
163
 
4.3%

Most occurring characters

ValueCountFrequency (%)
01407
95.7%
163
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01407
95.7%
163
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01407
95.7%
163
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01407
95.7%
163
 
4.3%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
1143 
1
327 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01143
77.8%
1327
 
22.2%

Length

2025-12-30T23:12:37.977409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-30T23:12:38.150899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01143
77.8%
1327
 
22.2%

Most occurring characters

ValueCountFrequency (%)
01143
77.8%
1327
 
22.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01143
77.8%
1327
 
22.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01143
77.8%
1327
 
22.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01143
77.8%
1327
 
22.2%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
797 
1
673 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1470
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0797
54.2%
1673
45.8%

Length

2025-12-30T23:12:38.388255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-30T23:12:38.550786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0797
54.2%
1673
45.8%

Most occurring characters

ValueCountFrequency (%)
0797
54.2%
1673
45.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0797
54.2%
1673
45.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0797
54.2%
1673
45.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0797
54.2%
1673
45.8%

Interactions

2025-12-30T23:12:13.040756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:29.557423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:32.551014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:35.252970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:38.746960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:41.691141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:44.967198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:00.055073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:03.166454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:06.180861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:09.433537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:13.339307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:29.848215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:32.805919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:35.515872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:39.012859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:41.971317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:45.279066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:00.339571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:03.437938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:06.431452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:09.720918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:13.600994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:30.120667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:33.078254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:35.759802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:39.243072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:42.287627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:45.550525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:00.654838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:03.679003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:06.749852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:10.079393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:13.938234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:30.396174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:33.334892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:36.172869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:39.483835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:42.557461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:45.862216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:00.944849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:03.938812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:07.028553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:10.439204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:14.271588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:30.648944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:33.566172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:36.592983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:39.752978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:42.843244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:46.178534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:01.220031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:04.234816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:07.303669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:10.695699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:14.589418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:30.932287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:33.805241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:36.890072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:40.044727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:43.144457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:46.494467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:01.513525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:04.487194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:07.572093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:11.024590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:14.897231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:31.192296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:34.047782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:37.161694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:40.303690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:43.406740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:46.790839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:01.787242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:04.756797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:07.896301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:11.405948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:15.212755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:31.469791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:34.325951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:37.408560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:40.592012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:43.656767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:47.101062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:02.113632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:05.037904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:08.289295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:11.728375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:15.547475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:31.707555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:34.544331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:37.696604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:40.849349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:43.973437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:59.143237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:02.389339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:05.300862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:08.584611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:11.996540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:15.861580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:31.955886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:34.767931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:37.981183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:41.160767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:44.307785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:59.426611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:02.614542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:05.605252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:08.843835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:12.340262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:16.176355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:32.240212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:34.984851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:38.336888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:41.425702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:44.585403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:11:59.750373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:02.891669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:05.893833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:09.116249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-30T23:12:12.623938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-30T23:12:38.942840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
a_quitte_l_entrepriseageannee_experience_totaleannees_dans_l_entrepriseannees_dans_le_poste_actuelannees_depuis_la_derniere_promotionannes_sous_responsable_actuelaugementation_salaire_precedentedepartement_Consultingdepartement_Ressources Humainesdistance_domicile_travaildomaine_etude_Entrepreunariatdomaine_etude_Infra & Clouddomaine_etude_Marketingdomaine_etude_Ressources Humainesdomaine_etude_Transformation Digitalefrequence_deplacement_Frequentfrequence_deplacement_Occasionnelgenreheure_supplementairesnb_formations_suiviesniveau_educationniveau_hierarchique_postenombre_experiences_precedentesnombre_participation_peenote_evaluation_actuellenote_evaluation_precedenteposte_Cadre Commercialposte_Consultantposte_Directeur Techniqueposte_Managerposte_Représentant Commercialposte_Ressources Humainesposte_Senior Managerposte_Tech Leadrevenu_mensuelsatisfaction_employee_environnementsatisfaction_employee_equilibre_pro_persosatisfaction_employee_equipesatisfaction_employee_nature_travailstatut_marital_Divorcé(e)statut_marital_Marié(e)
a_quitte_l_entreprise1.0000.2130.2080.1730.1690.0270.1790.0000.0790.0000.0670.0610.0160.0460.0140.0370.1100.0400.0090.2430.0790.0000.2160.1070.1980.0000.1320.0000.0920.0810.0710.1510.0170.0750.0760.2170.1150.0950.0390.0990.0810.085
age0.2131.0000.6570.2520.1980.1740.1950.0080.0000.000-0.0190.0330.0270.0000.0000.0000.0580.0480.0000.0000.0000.1530.2950.3530.0930.0000.0250.1120.1400.2000.1060.2280.0360.3130.0910.4720.0060.0330.0350.0000.0940.113
annee_experience_totale0.2080.6571.0000.5940.4930.3350.495-0.0260.0200.038-0.0030.0000.0000.0590.0000.0660.0170.0000.0000.000-0.0140.0950.5390.3150.0640.0000.0000.2130.2150.3300.1770.3390.0390.5720.1030.7100.0000.0000.0310.0240.0000.065
annees_dans_l_entreprise0.1730.2520.5941.0000.8540.5200.843-0.0540.0000.0000.0110.0000.0000.0000.0000.0140.0140.0000.0660.0180.0010.0710.353-0.1710.0120.0000.0530.1550.1320.1710.0730.1990.0000.4150.0610.4640.0310.0200.0000.0000.0000.008
annees_dans_le_poste_actuel0.1690.1980.4930.8541.0000.5060.725-0.0260.0000.0000.0140.0000.0140.0510.0000.0000.0000.0000.0790.0420.0050.0290.241-0.1280.0230.0310.0000.1240.1230.1850.0950.1430.0000.1930.0590.3950.0360.0250.0000.0000.0000.054
annees_depuis_la_derniere_promotion0.0270.1740.3350.5200.5061.0000.467-0.0550.0000.000-0.0050.0000.0000.0000.0000.0000.0630.0000.0000.0110.0100.0000.206-0.0670.0560.0000.0000.0610.0910.1000.1090.0430.0000.2480.0330.2650.0000.0000.0500.0000.0120.068
annes_sous_responsable_actuel0.1790.1950.4950.8430.7250.4671.000-0.0260.0000.0000.0040.0000.0000.0290.0000.0000.0890.0900.0000.000-0.0120.0000.232-0.1440.0300.0300.0440.0890.0890.1530.0000.1550.0000.2070.0720.3650.0000.0310.0000.0000.0000.000
augementation_salaire_precedente0.0000.008-0.026-0.054-0.026-0.055-0.0261.0000.0480.0000.0300.0320.0000.0000.0000.0000.0030.0600.0490.000-0.0040.0210.0000.0000.0000.9970.0360.0000.0000.0000.0080.0370.0000.0680.000-0.0340.0000.0000.0270.0000.0000.000
departement_Consulting0.0790.0000.0200.0000.0000.0000.0000.0481.0000.2860.0000.0250.1230.4760.1810.1800.0000.0000.0000.0000.0350.0000.2470.0000.0000.0160.0000.7320.3340.1700.2240.3320.2580.0630.2370.2270.0220.0540.0250.0000.0210.000
departement_Ressources Humaines0.0000.0000.0380.0000.0000.0000.0000.0000.2861.0000.0000.0000.0590.0630.6340.0380.0000.0000.0190.0000.0000.0000.0900.0600.0000.0000.0000.1060.0900.0350.0540.0360.8960.0770.0590.0700.0250.0500.0000.0520.0000.017
distance_domicile_travail0.067-0.019-0.0030.0110.014-0.0050.0040.0300.0000.0001.0000.0210.0000.0000.0000.0000.0340.0440.0300.066-0.0250.0000.054-0.0100.0150.0580.0280.0230.0000.0440.0000.0000.0000.0410.0000.0030.0000.0000.0250.0000.0000.000
domaine_etude_Entrepreunariat0.0610.0330.0000.0000.0000.0000.0000.0320.0250.0000.0211.0000.2590.1020.0220.2090.0000.0000.0000.0000.0450.0000.0690.0130.0000.0000.0000.0500.0000.0000.0000.0450.0000.0220.0000.0410.0000.0080.0390.0000.0000.000
domaine_etude_Infra & Cloud0.0160.0270.0000.0000.0140.0000.0000.0000.1230.0590.0000.2591.0000.2880.1060.5670.0130.0120.0000.0000.0000.0000.0000.0910.0000.0000.0000.0860.0340.0000.0050.0310.0530.0000.0420.0000.0170.0430.0000.0440.0000.000
domaine_etude_Marketing0.0460.0000.0590.0000.0510.0000.0290.0000.4760.0630.0000.1020.2881.0000.0300.2330.0000.0190.0000.0000.0550.0640.1720.0720.0000.0000.0290.4540.1560.0740.1020.1260.0550.0000.1080.1500.0000.0090.0670.0550.0000.000
domaine_etude_Ressources Humaines0.0140.0000.0000.0000.0000.0000.0000.0000.1810.6340.0000.0220.1060.0301.0000.0830.0000.0000.0000.0000.0000.0490.0640.0630.0550.0000.0180.0620.0500.0000.0220.0000.5360.0670.0260.0640.0290.0270.0260.0000.0000.045
domaine_etude_Transformation Digitale0.0370.0000.0660.0140.0000.0000.0000.0000.1800.0380.0000.2090.5670.2330.0831.0000.0000.0000.0000.0000.0940.0610.0350.0550.0530.0000.0000.1290.0590.0540.0180.0410.0290.0000.0200.0160.0410.0180.0370.0000.0000.000
frequence_deplacement_Frequent0.1100.0580.0170.0140.0000.0630.0890.0030.0000.0000.0340.0000.0130.0000.0000.0001.0000.7510.0000.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0450.0000.0290.0000.0470.0000.0000.0000.0000.0000.013
frequence_deplacement_Occasionnel0.0400.0480.0000.0000.0000.0000.0900.0600.0000.0000.0440.0000.0120.0190.0000.0000.7511.0000.0000.0000.0000.0000.0310.0000.0000.0000.0130.0000.0000.0170.0000.0000.0000.0000.0000.0380.0000.0000.0000.0020.0320.047
genre0.0090.0000.0000.0660.0790.0000.0000.0490.0000.0190.0300.0000.0000.0000.0000.0000.0000.0001.0000.0310.0000.0000.0480.0000.0000.0000.0000.0000.0610.0000.0000.0000.0190.0170.0570.0460.0000.0000.0000.0000.0360.000
heure_supplementaires0.2430.0000.0000.0180.0420.0110.0000.0000.0000.0000.0660.0000.0000.0000.0000.0000.0090.0000.0311.0000.0990.0010.0000.0000.0000.0000.0000.0000.0340.0000.0000.0000.0000.0000.0000.0000.0600.0000.0250.0220.0000.000
nb_formations_suivies0.0790.000-0.0140.0010.0050.010-0.012-0.0040.0350.000-0.0250.0450.0000.0550.0000.0940.0000.0000.0000.0991.0000.0270.017-0.0470.0000.0000.0130.0000.0000.0000.0000.0500.0000.0000.033-0.0350.0000.0000.0000.0210.0000.032
niveau_education0.0000.1530.0950.0710.0290.0000.0000.0210.0000.0000.0000.0000.0000.0640.0490.0610.0000.0000.0000.0010.0271.0000.0880.1010.0270.0000.0000.0470.0430.0580.0000.0940.0000.0000.0000.0940.0190.0000.0160.0150.0000.000
niveau_hierarchique_poste0.2160.2950.5390.3530.2410.2060.2320.0000.2470.0900.0540.0690.0000.1720.0640.0350.0000.0310.0480.0000.0170.0881.0000.1130.0690.0000.0000.4830.3960.4490.2750.2730.1010.6570.2870.8640.0000.0000.0000.0000.0000.028
nombre_experiences_precedentes0.1070.3530.315-0.171-0.128-0.067-0.1440.0000.0000.060-0.0100.0130.0910.0720.0630.0550.0000.0000.0000.000-0.0470.1010.1131.0000.0000.0000.0000.0420.0680.1200.0710.1060.0660.0850.0380.1900.0000.0510.0000.0000.0250.026
nombre_participation_pee0.1980.0930.0640.0120.0230.0560.0300.0000.0000.0000.0150.0000.0000.0000.0550.0530.0000.0000.0000.0000.0000.0270.0690.0001.0000.0000.0220.0180.0320.0000.0000.0250.0000.0800.0000.0560.0000.0190.0300.0000.4630.395
note_evaluation_actuelle0.0000.0000.0000.0000.0310.0000.0300.9970.0160.0000.0580.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0290.0000.0180.0000.0000.0000.0110.0050.0000.0000.0000.0000.0260.0000.000
note_evaluation_precedente0.1320.0250.0000.0530.0000.0000.0440.0360.0000.0000.0280.0000.0000.0290.0180.0000.0000.0130.0000.0000.0130.0000.0000.0000.0220.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0460.0340.0000.0000.0000.0220.015
poste_Cadre Commercial0.0000.1120.2130.1550.1240.0610.0890.0000.7320.1060.0230.0500.0860.4540.0620.1290.0000.0000.0000.0000.0000.0470.4830.0420.0180.0290.0001.0000.2430.1220.1620.1240.0940.1400.1720.4620.0000.0000.0470.0230.0000.000
poste_Consultant0.0920.1400.2150.1320.1230.0910.0890.0000.3340.0900.0000.0000.0340.1560.0500.0590.0000.0000.0610.0340.0000.0430.3960.0680.0320.0000.0000.2431.0000.1040.1390.1060.0800.1200.1480.3790.0000.0150.0590.0000.0000.000
poste_Directeur Technique0.0810.2000.3300.1710.1850.1000.1530.0000.1700.0350.0440.0000.0000.0740.0000.0540.0000.0170.0000.0000.0000.0580.4490.1200.0000.0180.0000.1220.1041.0000.0650.0450.0270.0540.0700.5460.0310.0000.0000.0000.0220.000
poste_Manager0.0710.1060.1770.0730.0950.1090.0000.0080.2240.0540.0000.0000.0050.1020.0220.0180.0000.0000.0000.0000.0000.0000.2750.0710.0000.0000.0000.1620.1390.0651.0000.0660.0470.0760.0960.2940.0000.0000.0000.0000.0000.000
poste_Représentant Commercial0.1510.2280.3390.1990.1430.0430.1550.0370.3320.0360.0000.0450.0310.1260.0000.0410.0450.0000.0000.0000.0500.0940.2730.1060.0250.0000.0000.1240.1060.0450.0661.0000.0290.0550.0710.2910.0320.0320.0000.0190.0420.000
poste_Ressources Humaines0.0170.0360.0390.0000.0000.0000.0000.0000.2580.8960.0000.0000.0530.0550.5360.0290.0000.0000.0190.0000.0000.0000.1010.0660.0000.0000.0000.0940.0800.0270.0470.0291.0000.0370.0510.0780.0240.0570.0210.0370.0000.008
poste_Senior Manager0.0750.3130.5720.4150.1930.2480.2070.0680.0630.0770.0410.0220.0000.0000.0670.0000.0290.0000.0170.0000.0000.0000.6570.0850.0800.0110.0000.1400.1200.0540.0760.0550.0371.0000.0820.7270.0000.0000.0000.0000.0000.039
poste_Tech Lead0.0760.0910.1030.0610.0590.0330.0720.0000.2370.0590.0000.0000.0420.1080.0260.0200.0000.0000.0570.0000.0330.0000.2870.0380.0000.0050.0000.1720.1480.0700.0960.0710.0510.0821.0000.2720.0390.0000.0290.0060.0000.000
revenu_mensuel0.2170.4720.7100.4640.3950.2650.365-0.0340.2270.0700.0030.0410.0000.1500.0640.0160.0470.0380.0460.000-0.0350.0940.8640.1900.0560.0000.0460.4620.3790.5460.2940.2910.0780.7270.2721.0000.0000.0000.0430.0000.0490.044
satisfaction_employee_environnement0.1150.0060.0000.0310.0360.0000.0000.0000.0220.0250.0000.0000.0170.0000.0290.0410.0000.0000.0000.0600.0000.0190.0000.0000.0000.0000.0340.0000.0000.0310.0000.0320.0240.0000.0390.0001.0000.0000.0000.0000.0000.051
satisfaction_employee_equilibre_pro_perso0.0950.0330.0000.0200.0250.0000.0310.0000.0540.0500.0000.0080.0430.0090.0270.0180.0000.0000.0000.0000.0000.0000.0000.0510.0190.0000.0000.0000.0150.0000.0000.0320.0570.0000.0000.0000.0001.0000.0000.0000.0280.000
satisfaction_employee_equipe0.0390.0350.0310.0000.0000.0500.0000.0270.0250.0000.0250.0390.0000.0670.0260.0370.0000.0000.0000.0250.0000.0160.0000.0000.0300.0000.0000.0470.0590.0000.0000.0000.0210.0000.0290.0430.0000.0001.0000.0000.0330.025
satisfaction_employee_nature_travail0.0990.0000.0240.0000.0000.0000.0000.0000.0000.0520.0000.0000.0440.0550.0000.0000.0000.0020.0000.0220.0210.0150.0000.0000.0000.0260.0000.0230.0000.0000.0000.0190.0370.0000.0060.0000.0000.0000.0001.0000.0000.000
statut_marital_Divorcé(e)0.0810.0940.0000.0000.0000.0120.0000.0000.0210.0000.0000.0000.0000.0000.0000.0000.0000.0320.0360.0000.0000.0000.0000.0250.4630.0000.0220.0000.0000.0220.0000.0420.0000.0000.0000.0490.0000.0280.0330.0001.0000.489
statut_marital_Marié(e)0.0850.1130.0650.0080.0540.0680.0000.0000.0000.0170.0000.0000.0000.0000.0450.0000.0130.0470.0000.0000.0320.0000.0280.0260.3950.0000.0150.0000.0000.0000.0000.0000.0080.0390.0000.0440.0510.0000.0250.0000.4891.000

Missing values

2025-12-30T23:12:16.806295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-30T23:12:18.673361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

satisfaction_employee_environnementnote_evaluation_precedenteniveau_hierarchique_postesatisfaction_employee_nature_travailsatisfaction_employee_equipesatisfaction_employee_equilibre_pro_personote_evaluation_actuelleheure_supplementairesaugementation_salaire_precedenteagegenrerevenu_mensuelnombre_experiences_precedentesannee_experience_totaleannees_dans_l_entrepriseannees_dans_le_poste_actuela_quitte_l_entreprisenombre_participation_peenb_formations_suiviesdistance_domicile_travailniveau_educationannees_depuis_la_derniere_promotionannes_sous_responsable_actueldomaine_etude_Entrepreunariatdomaine_etude_Infra & Clouddomaine_etude_Marketingdomaine_etude_Ressources Humainesdomaine_etude_Transformation Digitalefrequence_deplacement_Frequentfrequence_deplacement_Occasionnelposte_Cadre Commercialposte_Consultantposte_Directeur Techniqueposte_Managerposte_Représentant Commercialposte_Ressources Humainesposte_Senior Managerposte_Tech Leaddepartement_Consultingdepartement_Ressources Humainesstatut_marital_Divorcé(e)statut_marital_Marié(e)
023241131114115993886410012050100001100000000000
13222434023490513011010701381170100010000000001001
242132331153702090670010322000000001010000001000
343133331113312909188700334300100010000000001001
413124330122703468962201321220000101010000001001
543143230133203068087700222360100010010000001000
6341112412059126704121003333000000101010000001001
7431323402230026931110012241000100001010000001010
84233234021380952601097002233180100010000000011000
93323223013360523761777023273770000101000100001001
satisfaction_employee_environnementnote_evaluation_precedenteniveau_hierarchique_postesatisfaction_employee_nature_travailsatisfaction_employee_equipesatisfaction_employee_equilibre_pro_personote_evaluation_actuelleheure_supplementairesaugementation_salaire_precedenteagegenrerevenu_mensuelnombre_experiences_precedentesannee_experience_totaleannees_dans_l_entrepriseannees_dans_le_poste_actuela_quitte_l_entreprisenombre_participation_peenb_formations_suiviesdistance_domicile_travailniveau_educationannees_depuis_la_derniere_promotionannes_sous_responsable_actueldomaine_etude_Entrepreunariatdomaine_etude_Infra & Clouddomaine_etude_Marketingdomaine_etude_Ressources Humainesdomaine_etude_Transformation Digitalefrequence_deplacement_Frequentfrequence_deplacement_Occasionnelposte_Cadre Commercialposte_Consultantposte_Directeur Techniqueposte_Managerposte_Représentant Commercialposte_Ressources Humainesposte_Senior Managerposte_Tech Leaddepartement_Consultingdepartement_Ressources Humainesstatut_marital_Divorcé(e)statut_marital_Marié(e)
1460421121301429137851554003284040000101000000001000
146142312331135001085442032113283200010001100000000010
1462224412301139112031021209012241960010001100000000001
1463232123301931099360109400253170000100000000011000
146442134330182612966054200253000000001000010000000
14653424333017360257141752013232030000110010000001001
146642311330153909991497701561170000101000100001001
146724222341202706142166201043030100001000000011001
1468422242301449053902179600323080000110100000000001
146924231430123404404264300383120000101010000001001